AionLabs: Aion-3.0
Aion-3.0 is a multi-model roleplaying and storytelling system from AionLabs, built on the GLM family of models. It uses a collaborative generation process in which multiple specialized models each contribute...
Anyone in the Project can @-mention AionLabs: Aion-3.0 with the team's shared context - pooled credits, one chat, one memory.
Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.
Verdict
Best for
- Long-context document processing tasks
- Teams exploring alternative providers
- Cost-conscious multi-document analysis
- Internal eval and comparison testing
Strengths
The 128K context window handles substantial documents, transcripts, or codebases in a single pass. Pricing sits below premium models while maintaining a large context capacity, making it viable for high-volume long-context workloads. As a newer entrant from AionLabs, it may offer differentiated behavior patterns worth testing against incumbent models for specific use cases.
Trade-offs
No public benchmarks means you're flying blind on reasoning quality, instruction-following, and domain-specific accuracy. At $6/Mtok output, it costs more than GPT-4o Mini ($0.60) and Gemini 1.5 Flash ($0.30) while lacking their proven track records. The 128K window trails Gemini 1.5 Pro's 2M tokens for extreme long-context needs. Teams need to invest time in custom evals to understand where it excels or falls short.
Specifications
- Provider
- aion-labs
- Category
- llm
- Context length
- 131,072 tokens
- Max output
- 32,768 tokens
- Modalities
- text
- License
- proprietary
- Released
- 2026-07-07
Pricing
- Input
- $3.00/Mtok
- Output
- $6.00/Mtok
- Model ID
aion-labs/aion-3.0
Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool - one plan, one balance for everyone.
Team cost calculator
5 seats · 80 msgs/day
Switchy meters this against your org's shared credit pool - one plan, one balance for everyone.
Providers
Performance
Benchmarks
Works well with
Top MCPs
Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.
How Switchy teams use it
Starter prompts
Multi-Document Synthesis
I'm providing three research papers below. Identify the two core methodological differences between them and explain which approach would scale better for real-time systems. Papers: [paste documents]Open in a Project →
Codebase Navigation
Here's a Python project with five modules. Trace how the `process_request` function in api.py calls through to the database layer and list every validation step along the way. [paste code]Open in a Project →
Meeting Transcript Analysis
Summarize this 90-minute meeting transcript into three sections: decisions made, action items with owners, and unresolved questions. Use bullet points. [paste transcript]Open in a Project →
Contract Clause Extraction
Extract all liability limitation clauses from this service agreement and rewrite each in plain language. Note any ambiguous terms that need clarification. [paste contract]Open in a Project →
Comparative Benchmark Eval
Generate five multiple-choice questions (with answers) that test reasoning about causal relationships in supply chain logistics. Make them challenging enough to differentiate model quality.Open in a Project →